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The Use of Phosphoproteomic Data to Identify Altered Kinases and Signaling Pathways in Cancer

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The Use of Phosphoproteomic Data to Identify Altered Kinases and Signaling Pathways in Cancer The use of phosphoproteomic data to identify altered kinases and signaling pathways in cancer By Sara Renee Savage Thesis Submitted to the Faculty of the Graduate School of Vanderbilt University in partial fulfillment of the requirements for the degree of MASTER OF SCIENCE in Biomedical Informatics August 10, 2018 Nashville, Tennessee Approved: Bing Zhang, Ph.D. Carlos Lopez, Ph.D. Qi Liu, Ph.D. ACKNOWLEDGEMENTS The work presented in this thesis would not have been possible without the funding provided by the NLM training grant (T15-LM007450) and the support of the Biomedical Informatics department at Vanderbilt. I am particularly indebted to Rischelle Jenkins, who helped me solve all administrative issues. Furthermore, this work is the result of a collaboration between all members of the Zhang lab and the larger CPTAC consortium. I would like to thank the other CPTAC centers for processing the data, and Chen Huang and Suhas Vasaikar in the Zhang lab for analyzing the colon cancer copy number and proteomic data, respectively. All members of the Zhang lab have been extremely helpful in answering any questions I had and offering suggestions on my work. Finally, I would like to acknowledge my mentor, Bing Zhang. I am extremely grateful for his guidance and for giving me the opportunity to work on these projects. ii TABLE OF CONTENTS Page ACKNOWLEDGEMENTS ................................................................................................ ii LIST OF TABLES............................................................................................................. v LIST OF FIGURES ......................................................................................................... vi LIST OF ABBREVIATIONS ...........................................................................................viii Chapter 1. Introduction ................................................................................................................. 1 Dysregulation of Cellular Components of Cancer ............................................ 1 Multi-Omics Integration in Cancer .................................................................... 2 Kinases and Phosphatases in Cancer ............................................................. 3 The Use of Phosphoproteomic Data to Study Kinase Signaling ...................... 3 2. Overview of Resources for Studying Kinases, Phosphatases, and Phosphorylation Sites: Tools for Analyzing Phosphoproteomic Data .................................................... 5 Introduction ...................................................................................................... 5 Methods ........................................................................................................... 6 Results ............................................................................................................. 8 Discussion ..................................................................................................... 23 3. Proteomic Landscape of Kinase Signaling in Cancer ............................................... 26 Introduction .................................................................................................... 26 Methods ......................................................................................................... 27 Results ........................................................................................................... 29 Discussion ..................................................................................................... 36 4. Characterization of Molecular Subtype-Specific Driver Subnetworks in Breast Cancer ...................................................................................................................... 38 Introduction .................................................................................................... 38 Methods ......................................................................................................... 39 Results ........................................................................................................... 40 Discussion ..................................................................................................... 47 iii 5. Phosphoproteomic Data Reveal a Dual Role for RB1 in Colon Cancer.................... 48 Introduction .................................................................................................... 48 Methods ......................................................................................................... 49 Results ........................................................................................................... 51 Discussion ..................................................................................................... 59 6. Conclusions and future directions............................................................................. 61 Summary ....................................................................................................... 61 Evaluation of Technology............................................................................... 61 Kinase Signaling in Cancer ............................................................................ 62 Kinase Signaling Comparison Among Tissues .............................................. 62 Appendix A. ROC curve for substrate prediction of PKC .............................................................. 63 B. Website URLs for bioinformatics tools ...................................................................... 64 C. Breast cancer subtype driver subnetworks ............................................................... 67 D. WikiPathways enrichment results for subtype driver subnetworks ........................... 71 E. GO BP enrichment for proteins with altered phosphorylation in colon cancer .......... 74 F. Colon cancer-associated phosphorylation sites ....................................................... 75 REFERENCES .............................................................................................................. 77 iv LIST OF TABLES Table Page 1. Knowledge bases of human kinases and phosphatases ........................................... 9 2. Databases of phosphorylation sites ......................................................................... 10 3. Available phosphorylation site and kinase-substrate prediction tools ...................... 14 4. Kinase activity prediction and phosphoproteomic dataset analysis tools ................ 18 5. Resources for studying the effect of mutations on kinases and phosphorylation sites ......................................................................................................................... 21 6. Kinase-inhibitor relationship resources .................................................................... 22 7. Miscellaneous kinase signaling tools ....................................................................... 22 8. Number of identified proteins in three cancer datasets ........................................... 29 9. Predicted proteogenomic mutation effect from ReKINect ........................................ 36 10. Colon phosphoproteomic data by the numbers ....................................................... 51 v LIST OF FIGURES Figure Page 1. Network of phosphorylation site and kinase-substrate interaction databases ......... 11 2. Number of substrates per kinase and phosphatase ................................................ 13 3. Network of phosphorylation site predictor tools and the resources used to make predictions ............................................................................................................... 16 4. ROC curves for substrate prediction of four kinases ............................................... 17 5. True and false positive predictions for kinase activity prediction tools ..................... 19 6. Kinase activity AUC for the GSEA and z score methods using various substrate sets .......................................................................................................................... 20 7. Intersection of detected kinases and phosphatases in three different cancer types ........................................................................................................................ 29 8. Enzymes in CPTAC and HPA data ......................................................................... 30 9. Number of amino acids in proteins that were and were not detected by mass spectrometry ............................................................................................................ 31 10. Log2 mRNA expression of proteins that were and were not detected by mass spectrometry ............................................................................................................ 32 11. Heatmap of inferred kinase and phosphatase activity scores for breast cancer samples ................................................................................................................... 34 12. Multi-omics profile of MERTK and NT5C in breast cancer ...................................... 35 13. Venn diagram of significant nodes in subtype-specific driver subnetworks ............. 40 14. Relative differential phosphorylation abundance in breast cancer subtypes ........... 42 vi 15. Overlap of the basal driver subnetwork and the DNA damage response pathway .. 43 16. Nodes unique to the basal subnetwork.................................................................... 44
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